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Online Stochastic Combinatorial Optimization

Autor: Pascal Van Hentenryck, Russell Bent

Jazyk:

Vazba: Brožovaná

Vydavatel: MIT Press Ltd

Dostupnost: 50 % šance

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Online decision making under uncertainty and time constraints represents one of the most challenging...

Informace o knize

Autor

Pascal Van Hentenryck, Russell Bent

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2009

Stránek

248

EAN

9780262513470

ISBN

0262513471

Enbook ID

04561532

Vydavatel

MIT Press Ltd

Hmotnost

430

Rozměry

195 x 223 x 13

Kompletní popis

Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

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